IRS-assisted AmBC wireless body area network resource optimization method

Abstract

The invention relates to an IRS-assisted AmBC wireless body area network resource optimization method, and belongs to the technical field of wireless body area networks, and the method comprises the following steps: S1, constructing an IRS-enhanced downlink AmBC-WBAN communication system; s2, minimizing the transmission power of the BS under the constraint of ensuring QoS of all nodes, and constructing an optimization problem; s3, decomposing the original problem into two sub-problems by adopting an alternating iterative optimization algorithm: base station beam forming optimization and IRS reflection coefficient matrix optimization; solving the two sub-problems by adopting a positive semi-definite relaxation (SDR) and a successive approximation method (SCA) respectively; the updating of the solutions is performed in an alternating manner until convergence.

Description

Technical Field

[0001] This invention belongs to the field of wireless body area network technology and relates to an IRS-assisted AmBC wireless body area network resource optimization method. Background Technology

[0002] Despite the significant advantages of AmBC technology in terms of power efficiency, its application in a single WBAN system still faces numerous challenges. First, ambient radio frequency signals are susceptible to severe attenuation and multipath effects during propagation, leading to difficulties in energy management of AmBC nodes. Second, the communication capacity of AmBC links is inherently limited, making it difficult to meet the stringent requirements for transmission quality and real-time performance in medical monitoring scenarios, highlighting the need for enhanced communication reliability. It is worth noting that previous research primarily focused on single WBAN monitoring scenarios, assuming no interference between WBANs of different cellular users. However, with the increasing demand for health monitoring in daily life, multi-user coexistence scenarios (where inter-network interference occurs between multiple WBANs) are becoming increasingly common. This evolution places higher demands on multi-user concurrent access and interference management capabilities. Therefore, there is an urgent need to develop a new system framework capable of simultaneously achieving ultra-low power consumption and reliable communication in multiple coexisting WBANs. Summary of the Invention

[0003] In view of this, the purpose of this invention is to provide an IRS-assisted AmBC wireless body area network resource optimization method.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] An IRS-assisted AmBC wireless body area network resource optimization method includes the following steps: S1: Construct an IRS-enhanced downlink AmBC-WBAN communication system; S2: Under the constraint of ensuring the QoS of all nodes, minimize the transmission power of the BS and construct an optimization problem; S3: The original problem is decomposed into two sub-problems using an alternating iterative optimization algorithm: base station beamforming optimization and IRS reflection coefficient matrix optimization. For these two sub-problems, the positive semidefinite relaxation method (SDR) and the continuous approximation method (SCA) are used to solve them respectively. The solution is updated in an alternating manner until convergence.

[0006] Furthermore, the IRS-enhanced downlink AmBC-WBAN communication system described in step S1 involves multiple wireless body area networks (AmBC-WBANs) based on environmental backscatter communication; the system includes a network equipped with... M A base station BS with multiple antennas, which simultaneously serves as a time slot in the same time slot. K Provides service to 1,000 cellular users; each user is equipped withS Each internal or external sensor node forms a WBAN, with its handheld mobile terminal acting as the central node, while also directly receiving information from the BS, thus the system has a total of K A separate WBAN; The system adopts a symbiotic wireless communication architecture. The main signal transmitted by the base station has two functions: to facilitate the main communication link between the BS and the mobile terminal; and to achieve energy-efficient information transmission by backscattering through the AmBC sensor nodes in the WBAN. The system includes a component consisting of N An IRS consisting of programmable reflective units is used to enhance multiple communication links, including the link from the BS to the central node and the link from the sensor to the central node, all of which are optimized through intelligent beam control. Assumption and These represent the set of cellular users and the set of sensor nodes in each WBAN, respectively. The system operates in time-division multiple access mode, with each sensor node in a WBAN allocated a dedicated time slot, the duration of which is... Assuming all time slots have the same length, a normalized frame length is formed, i.e. The AmBC symbol period spans the main signal. L One symbol period, and the sensor nodes in each WBAN use on-off keyed modulation for backscatter communication; set up For the BS symbol period, The number of primary symbols spanned by one AmBC symbol; when using a normalized frame length and assuming each slot At that time, the normalized main symbol period must satisfy ; It is assumed that all Channel State Information (CSI) is fully known through channel estimation; in these assumptions, This indicates the channel from the BS to the IRS. Indicates the base station to the The channels of the central nodes of each WBAN, assuming within one time slot It is stable; assuming This represents the channel gain from the BS to all WBAN sensor nodes, where For base station to the The channel gain of each WBAN cellular user network sensor node, among which For base station to the The first WBAN cellular user network Channel gain of each sensing node; Indicates IRS to the kThe channels of the central node of a WBAN cellular user network, assuming that within a time slot It is stable; assume This represents the channel gain from the IRS to the sensor nodes of all WBAN cellular user networks, where It is the IRS to the k The channel gain of the sensor nodes in a WBAN cellular user network, where For IRS to the k The first WBAN s Channel gain of each sensing node; Indicates the first s Complex gain of the AmBC reflection channel from each sensor node to its corresponding WBAN cellular user network center node; Assuming there is a line-of-sight (LoS) channel between the IRS and the BS, It is modeled as a Ricean fading channel, as shown below. (1) in, Rice factor, The line-of-sight portion of the channel. The channel matrix representing NLoS has elements that follow an independent and identically distributed complex Gaussian distribution with a mean of zero and a variance of 1; the remaining channels will experience independent Rayleigh fading. In the time slot s In this context, the main signal sent by the BS is represented as: (2) in, It is the base station in the time slot s China to the first k Beamforming vectors of WBANs Cellular user k The symbol, within each symbol time T, It is changing; in time slots Down, The changes in numerical values ​​do not affect the analysis process; In the time slot s In China, cellular users k The received signal is represented as: (3) in, It is the reflection coefficient of all sensor nodes. The elements follow independent and identically distributed additive white Gaussian noise with a mean of zero and a variance of . ; Indicates the first k The first WBAN sInformation symbols of each sensor node; assumption and The probability of them occurring is equal. In the time slot s In this context, the reflection coefficient matrix of the IRS is expressed as: (4) in Indicates IRS reflective element n In the time slot s Phase shift in; the first k The signal-to-interference-plus-noise ratio (SINR) of the signal received by the central node of a WBAN is expressed by the following formula: (5) No. k The SINR of the signal received by the central node of each WBAN is further expressed by the following formula: (6) At the central node of each WBAN, continuous interference cancellation is used to remove components from the direct link and IRS link from the received composite signal, and then maximum ratio combining is used to reconstruct the remaining AmBC signal; k The first WBAN s The SINR of the signal decoded by each sensor node can be approximately expressed as: (7).

[0007] Furthermore, the problem can be optimized as follows: (8) Where C1 is the minimum QoS requirement for each sensor node across all WBANs, C1 represents the target SINR threshold; C2 represents the signal-to-noise ratio (SNR) of the central node in the WBAN cellular user network that must exceed the target SNR. C3 specifies the feasible range of IRS reflection phase offset.

[0008] Furthermore, in step S3, the base station beamforming optimization process includes: Treating the reflection coefficient matrix as constant, and optimizing only the beamforming vector scheme of the base station, the optimization problem becomes: (9) In iteration middle, By solving P1 and giving Optimization was performed using the semidefinite relaxation SDR method, which introduced auxiliary variables. , , , ,in, , ;when When the auxiliary variable is used, it is represented as: ;when When the auxiliary variable is used, it is represented as: ; auxiliary variables and Substituting (6), we get: (10) Then formula (10) is transformed into: (11) Similarly, Substituting into (7), the constraint condition becomes: (12) After the above variable substitutions and equation transformations, (P2) is transformed into a convex problem (P2.1), which can be solved using the CVX toolbox: (13) Furthermore, in step S3, the optimization process of the IRS reflection coefficient matrix includes: set up and ,definition and Equation (3) can be rewritten as: (14) in: ; In the In the next iteration, given , ,Will and Substituting into formula (7), we get: (15) in ; After the above transformation, constraint C1 in equation (8) is expressed as: (16) In equation (8), constraint C2 is expressed as: (17) Assumption

[0010]

[0011] The optimization problem can be further expressed as: (18) To obtain the rank-1 solution to problem P3, define Its equivalent rank-one constraint is expressed as: (19) An iterative approximation method based on the principal eigenvector is adopted, at the... In the next iteration, let For the previous solution The principal eigenvectors; the alternative problem is represented as: (20) By introducing auxiliary variables and the SCA method, the original problem P3 is transformed into a convex problem, which is solved using the standard Convex Optimization Toolbox (CVX). After decomposing the original problem into the two subproblems, these subproblems are solved iteratively until the result meets the convergence criterion. The obtained optimal value is considered as a lower bound, and feasible candidate solutions are extracted using eigenvalue decomposition (EVD). If the obtained solution does not satisfy the strict rank-one condition, Gaussian randomization is used to generate multiple randomized candidate solutions, and the one with the best performance is selected to ensure the feasibility and approximate optimality of the solution. Let Indicates the first The target value at the next iteration, i.e., the BS transmit power. When satisfied The algorithm terminates when the maximum number of iterations is reached.

[0012] Furthermore, the update process of the IRS reflection coefficient is simplified through a low-complexity algorithm, including: The IRS is divided into the primary link IRS (P-IRS) and the AmBC link IRS (B-IRS); the P-IRS is used to improve the primary communication link, and the B-IRS is used to enhance the AmBC link. One reflective unit is assigned to B-IRS, and the rest... One reflective unit is assigned to the P-IRS, where Indicates the integer operation; Assumption For the base station to B-IRS link, For base station to P-IRS; For the link from B-IRS to the WBAN network central node, This refers to the link from P-IRS to the central node of the WBAN network. For B-IRS to the k The first WBAN cellular user network s Channels of each sensor node For P-IRS to thek The first WBAN cellular user network s Channels of each sensor node This represents the reflection coefficient matrix of the B-IRS. This represents the reflection coefficient matrix of the P-IRS. Indicates time slot s The reflection coefficient vector at the B-IRS in the middle, Indicates time slot s The reflection coefficient vector at the P-IRS; To optimize the IRS reflection coefficient matrix, equation (6) is equivalently restated as: (twenty one) in, ;when and hour The situation is consistent, (21) with To analyze and obtain P-IRS ; Equation (7) is equivalently restated as: (twenty two) in, Extracting from the solution of P2 using eigenvalue decomposition , All signal components in the molecule are coherently combined to make maximize; Configured to maximize the corresponding equation by the following formula The received signal strength is ; Then put and Constructed into : (twenty three) in, .

[0013] The beneficial effects of this invention are as follows: This invention proposes a passive IRS-assisted cellular communication system framework, which is integrated with multiple AmBC-WBANs and formulates an optimization problem to minimize base station transmission power by jointly designing the IRS reflection coefficient matrix and the base station beamforming vector. This problem is very challenging due to its inherent non-convexity and the strong coupling between IRS reflection parameters and beamforming variables. Furthermore, this invention introduces a low-complexity implementation scheme, aiming to balance system performance and computational complexity.

[0014] This invention proposes an optimization algorithm based on block alternating iteration, which solves the original problem by decomposing it into two subproblems: active beamforming optimization of the base station, using semidefinite relaxation and singular value decomposition; and passive reflection optimization of the IRS, handled by the Successive convex approximation (SCA) method. This method effectively solves the non-convex constraint problem.

[0015] Comprehensive simulation experiments were conducted to evaluate the performance of the proposed algorithm within the established system framework. Results show that, under different configurations, including the number of base station antennas, the number of IRS elements, and varying QoS requirements for the central node and sensor nodes, the proposed optimization algorithm significantly reduces base station transmission power. Furthermore, the low-complexity version of the algorithm, implemented through the proposed simplified scheme, maintains competitive performance while reducing computational overhead.

[0016] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0017] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 A diagram of the downlink AmBC-WBAN communication system architecture for IRS enhancement; Figure 2 Flowchart of AmBC wireless body area network resource optimization method for IRS assistance; Figure 3 Configure for simulation; Figure 4 Convergence graphs for the Near-Optimal and Low-Complexity algorithms; Figure 5 The relationship between the BS transmit power and the minimum signal-to-noise ratio of the central node; Figure 6 The relationship between BS transmit power and the number of antennas; Figure 7 This represents the relationship between the BS transmit power and the minimum signal-to-noise ratio of the sensing node. Figure 8 This relates the BS transmit power to the number of IRS reflectors. Detailed Implementation

[0018] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0019] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0020] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0021] Example 1: This invention provides an IRS-assisted AmBC wireless body area network communication system, which involves multiple AmBC-WBANs (AmBC-WBANs based on environmental backscatter communication). For example... Figure 1 As shown, the system consists of a device equipped with M The BS consists of several antennas, and each BS is simultaneously active within the same time slot. K 1 cellular user is provided with service (each user is equipped with a mobile device). Each user is equipped with S Each internal (or external) sensor node forms a WBAN, with the handheld mobile terminal acting as the central node, while also directly receiving information from the BS. Therefore, the system has a total of... K A separate WBAN. The system adopts a symbiotic wireless communication architecture, in which the main signal transmitted by the base station has two functions: (1) it facilitates the main communication link between the BS and mobile terminals (which act as the central nodes of the WBAN); (2) the signal is backscattered through the AmBC sensor nodes in the WBAN to achieve energy-efficient information transmission. In addition, the system deploys a system consisting of NAn IRS consisting of programmable reflective units is used to enhance multiple communication links, including the link from the BS to the central node and the link from the sensor to the central node, all optimized through intelligent beam control.

[0022] Assumption and These represent the set of cellular users and the set of sensor nodes in each WBAN, respectively. The entire system operates in Time-Division Multiple Access (TDMA) mode, with each sensor node in a WBAN allocated a dedicated time slot, the duration of which is... To perform beamforming design, it is assumed that all time slots have the same length, forming a normalized frame length (i.e., The AmBC symbol period spans the main signal. L Each WBAN sensor node uses an on-off keying (OOK) modulation method for backscatter communication, and each WBAN sensor node uses an on-off keying (OOK) modulation method for backscatter communication.

[0023] set up The BS symbol period (unit: seconds). The number of primary symbols spanned by one AmBC symbol. This is when a normalized frame length (total frame length = 1) is used and each slot is assumed to be... When using standardized units, the period of the standardized primary symbol must satisfy the following condition: To ensure that a time slot contains at least Symbols (represented in standardized units).

[0024] It is assumed that all Channel State Information (CSI) is fully known through channel estimation. In these assumptions, This indicates the channel from the BS to the IRS. Indicates the base station to the The channels of the central nodes of each WBAN, assuming within a time slot It is stable. (Assume) This represents the channel gain from the BS to all WBAN sensor nodes, where For base station to the The channel gain of each WBAN cellular user network sensor node, among which For base station to the The first WBAN cellular user network The channel gain of each sensing node. Similarly, Indicates IRS to the k The channels of the central node of a WBAN cellular user network, assuming that within a time slot It is stable. Assume... This represents the channel gain from the IRS to the sensor nodes of all WBAN cellular user networks, where It is the IRS to the k The channel gain of the sensor nodes in a WBAN cellular user network, where For IRS to the k The first WBAN s The channel gain of each sensor node. Finally, Indicates the first s Complex gain of the AmBC reflection channel from each sensor node to its corresponding WBAN cellular user network center node.

[0025] Assuming the IRS and BS are sufficiently high, there exists a line-of-sight (LoS) channel between them. Therefore... It can be modeled as a Rician fading channel, as shown below. (1) in, Rician factor. The line-of-sight portion of the channel. Let represent the channel matrix of NLoS, whose elements follow an independent and identically distributed (i.i.d.) complex Gaussian distribution with a mean of zero and a variance of 1. The remaining channels will experience independent Rayleigh fading.

[0026] In the time slot s In this context, the main signal sent by the BS can be represented as: (2) in, It is the base station in the time slot s China to the first k Beamforming vectors of WBANs Cellular user k The symbol, within each symbol time T, It is subject to change. Because... Therefore, in time slots Down, The changing numerical values ​​do not affect the analysis process.

[0027] Therefore, in time slots s In China, cellular users k (that is, the first) k The signal received by the central node of each WBAN can be represented as: (3) in, It is the reflection coefficient of all sensor nodes (by default). ), The elements of are governed by additive white Gaussian noise that is independent and identically distributed (i.e., i.i.d.), with a mean of zero and a variance of . . Indicates the first k The first WBAN s Information symbols of each sensor node. Assume... and The probability of them occurring is equal.

[0028] In the time slot s In this context, the reflection coefficient matrix of the IRS can be expressed as: (4) in Indicates IRS reflective element n In the time slot s The phase shift in the signal. Due to path loss and reflection loss, signals reflected two or more times by the IRS are negligible. Then, the... k The signal-to-interference-plus-noise ratio (SINR) of the signals received by the central nodes of a WBAN can be expressed by the following formula: (5) because Each takes the value 0 or 1 with equal probability. k The SINR of the signal received by the central node of a WBAN can be further expressed by the following formula: (6) At the central node of each WBAN, Continuous Interference Cancellation (SIC) is used to remove components from the direct link and IRS link in the received composite signal, and then Maximum Ratio Combining (MRC) is used to reconstruct the remaining AmBC signal. Therefore, the... k The first WBAN s The SINR of the signal decoded by each sensor node can be approximately expressed as: (7) To minimize the power consumption of the BS while meeting the QoS requirements of all WBAN central nodes and sensor nodes, this invention aims to minimize the BS's transmission power while ensuring the QoS of all nodes. This objective is achieved by minimizing the BS's transmission power in each time slot. s Above the BS transmission beamforming vector and IRS reflection coefficient matrix This can be achieved through joint optimization. Therefore, the optimization problem can be formulated as: (8) Where C1 is the minimum QoS requirement for each sensor node across all WBANs, This represents the target SINR threshold. C2 represents the signal-to-noise ratio (SNR) of the central node in the WBAN cellular user network, which must exceed the target SNR. C3 specifies the feasible range for the IRS reflection phase offset. This is a multivariate coupled non-convex problem, making it difficult to obtain a globally optimal solution.

[0029] The overall algorithm framework is as follows Figure 2 As shown. Since the problem is non-convex and contains coupled variables, these variables need to be decoupled, and an iterative optimization method is required. To this end, this invention proposes an alternating iterative optimization algorithm that decomposes the original problem into two sub-problems: base station beamforming optimization and IRS reflection coefficient matrix optimization. These two sub-problems are solved using semidefinite relaxation (SDR) and continuous approximation method (SCA), respectively. The solutions are then updated alternately until convergence.

[0030] The beamforming vector optimization process is as follows: Treating the reflection coefficient matrix as constant, and optimizing only the beamforming vector scheme of the base station, the optimization problem becomes: (9) In iteration middle, This can be solved by (P1) and given... To optimize this process, and to address the non-convexity problem, this invention employs the semidefinite relaxation (SDR) method, introducing auxiliary variables. , , , .in, , Specifically, when In this case, the auxiliary variable can be represented as: .when In this case, the auxiliary variable can be represented as: .

[0031] auxiliary variables and Substituting (6), we get: (10) For each The following mandatory condition holds: the signal-to-noise ratio of each individual symbol is no less than γ. The complete derivation is as follows: In the time slot The average signal-to-noise ratio (SINR) constraint in the original text can be expressed as: (A.1) in, and Don't mean when and The numerator and denominator of the time signal-to-noise ratio (SINR). However, inequality (A.1) does not guarantee... and Established at the same time.

[0032] To ensure reliable detection under any symbol value, this invention employs a conservative transformation method: (A.2) Although this reconstruction is more stringent than Equation (A.1) and may lead to increased transmission power requirements, it still guarantees robustness in the worst case and provides more stringent QoS guarantees.

[0033] Based on equation (A.2), the SINR constraint can be further rewritten in a linear form: (A.3) At this point, the constraint is convex and can be directly embedded into the subsequent optimization framework.

[0034] Then formula (10) can be further transformed into: (11) Similarly, Substituting into (7), the constraint condition becomes: (12) After the above variable substitution and equation transformation, (P2) is transformed into a convex problem (P2.1), which can be solved using the CVX toolbox.

[0035] (13) The optimization process for the IRS reflection coefficient matrix is ​​as follows: After obtaining the optimal beamforming vector for the BS using the SDR-based solution, the next step is to optimize the IRS reflection coefficient matrix while keeping the beamforming vector fixed. Since the optimization variables no longer appear in problem (P2.1), the objective is to determine a set of feasible IRS coefficients that simultaneously satisfy the SINR requirements for both the main communication link and the AmBC link derived in previous stages. Furthermore, due to the separability of the IRS phase design over time intervals, this optimization can be performed independently for each time interval. To facilitate formalization, let's assume... and This invention defines and Therefore, equation (3) can be rewritten as: (14) in: .

[0036] In the In the next iteration, given , .Will and Substituting into formula (7), we get: (15) in .

[0037] After the above transformation, equation (8.c1) can be expressed as: (16) Similarly, constraint (8.c2) can be expressed as: (17) Assumption

[0038] The problem can then be further expressed as: (18) To obtain the rank-1 solution to problem (P3), define Its equivalent rank-one constraint can be expressed as: (19) To ensure the solvability of the problem, this invention employs an iterative approximation method based on the principal eigenvector. In the... In the next iteration, let For the previous solution The principal eigenvectors (unit norm). Therefore, the substitution problem can be expressed as: (20) By introducing auxiliary variables and the SCA method, the original problem (P3) is transformed into a convex problem, which can be solved using the standard Convex Optimization Toolbox (CVX). After decomposing the original problem into the two subproblems mentioned above, these subproblems are solved iteratively on an alternating basis until the result satisfies the convergence criterion. The obtained optimal values ​​are considered as lower bounds because the rank-one property is temporarily ignored. Once these optimal solutions are obtained, eigenvalue decomposition (EVD) is used to extract feasible candidate solutions. If the obtained solutions do not satisfy the strict rank-one condition, Gaussian randomization is used to generate multiple randomized candidate solutions, and the one with the best performance is selected to ensure the feasibility and approximate optimality of the solution. Let Indicates the first The target value at the next iteration (i.e., BS transmit power) When satisfied. The algorithm terminates when the maximum number of iterations is reached. The flowchart of the entire alternating optimization algorithm is shown in Algorithm 1.

[0039]

[0040] For question P2.1, the total number of variables is The total number of constraints is According to the interior-point method, the worst-case iterative complexity of the convex problem P2.1 can be analyzed from the total number of iterations and the complexity of each iteration, i.e. ,in Ignoring lower-order terms, the complexity can be expressed as For the optimization of problem P3.1, the total number of variables is... The total number of constraints is Therefore, the algorithmic complexity for solving problem P3.1 is O(n log n). ,in Ignoring lower-order terms, it can be expressed as Assume the total number of iterations required for the algorithm to converge is... The total complexity of finding the optimal solution to the original problem using the proposed method can be expressed as follows: .

[0041] Typically, the number of IRS reflector elements is much greater than the number of base station antennas, i.e. ( The computational complexity of optimizing the IRS reflection coefficient is far greater than that of optimizing the base station transmit beamforming vector. Furthermore, the inner loop iterations required when updating the IRS reflection coefficient further increase the overall computational burden. To address this issue, this invention proposes a low-complexity solution that simplifies the IRS reflection coefficient update process. Specifically, when the IRS is used to enhance a single communication link, the reflection coefficient can be updated using a closed-form solution. Inspired by this observation, this invention divides the IRS into two parts: the main link IRS (P-IRS) and the AmBC link IRS (B-IRS). The P-IRS is specifically used to improve the main communication link, while the B-IRS is used to enhance the AmBC link. For simplicity, this invention will... One reflective unit is assigned to B-IRS, and the rest... One reflective unit is assigned to the P-IRS, where This represents the floor function. Since the IRS is divided into two parts, the corresponding channel model must also be redefined accordingly.

[0042] Assumption This refers to the link from the base station to the B-IRS (P-IRS). This refers to the link from the B-IRS (P-IRS) to the central node of the WBAN network. For B-IRS (P-IRS) to the 1st k The first WBAN cellular user network s Channels for each sensor node. This represents the reflection coefficient matrix of B-IRS (P-IRS). Indicates time slot s The reflection coefficient vector at B-IRS (P-IRS).

[0043] To optimize the IRS reflection coefficient matrix, equation (6) is equivalently restated as: (twenty one) in, .when and hour The situation is consistent because only the P-IRS is affected by the central node. Therefore (21) Let's consider the analysis. We can obtain the P-IRS. .

[0044] Similarly, equation (7) is equivalently restated as: (twenty two) in, For low-complexity models, eigenvalue decomposition can be used to extract eigenvalues ​​from the solutions of P2. Strictly speaking, All signal components in the molecule should be coherently combined to make Maximize. However, This only represents a portion of the IRS reflective element. Therefore, Configured to maximize the corresponding equation by the following formula The received signal strength is .

[0045] Then put and Constructed into : (twenty three) in, .

[0046] Simulation settings such as Figure 3 As shown. Assume there are 2 WBAN cellular users. For further simplification, this invention assumes the distance from the BS (or IRS) to each WBAN central node is the same as the distance from the BS (or IRS) to the sensor nodes within the same WBAN.

[0047] The channel model under consideration consists of large-scale path loss components and small-scale fading components. The large-scale path loss component is modeled as follows: (29) in, The path loss is at a reference distance of 1 meter. This represents the distance between any two devices. The path loss index is set as follows: for paths from BS to IRS, BS to WBAN device, and IRS to WBAN device, respectively. All small-scale fading components in all channels are Rayleigh fading. Unless otherwise specified, other system parameter settings are as shown in Table 1.

[0048] Table 1

[0049] In addition, to ensure the duration of each time slot At least include The present invention requires a main symbol. If a normalized frame length convention is used... (i.e., the total frame length is 1), then the normalized main symbol period must satisfy... .

[0050] To demonstrate system performance, this invention considers two reference schemes for comparison: a random phase scheme and an IRS-free scheme. In the random phase scheme, the reflection coefficients of the IRS are randomly set. In the IRS-free scheme, no IRS is deployed in the system. In the following discussion, the proposed algorithm is referred to as the "NearOptimal Algorithm," and the proposed low-complexity model is referred to as the "Low-Complexity Model."

[0051] Figure 4 The convergence performance of the proposed Near-Optimal and Low-Complexity algorithms is presented. It can be seen that both algorithms exhibit fast convergence speeds, but the low-complexity algorithm has a slight advantage in convergence. This is because the low-complexity algorithm sacrifices some accuracy by simplifying the IRS partitioning, thus resulting in faster computation. The near-optimal algorithm, on the other hand, approximates the global optimum more accurately by alternately optimizing beamforming and the IRS reflection coefficient matrix. While its convergence speed is relatively slower, its final performance is better.

[0052] Figure 5 The relationship between base station transmit power and the minimum signal-to-noise ratio (SNR) of the center node in a WBAN cellular user network is demonstrated. When the minimum SNR of the center node increases from 6 dB to 16 dB, the base station transmit power of all schemes increases significantly. However, the proposed near-optimal algorithm requires only 5.3 dBm of transmit power to maintain the minimum SNR of the center node at 16 dB, while the scheme without IRS requires as much as 28 dBm. This indicates that IRS technology effectively reduces power consumption by enhancing the signal reflection path.

[0053] Figure 6 The number of base station antennas was analyzed. M Impact on base station transmit power. As the number of base station antennas increases from 4 to 24, the transmit power of the Near-Optimal scheme decreases from 13 dBm to 7.5 dBm. This is because more antennas provide greater beamforming freedom, allowing the beam to focus energy more precisely, thus reducing power requirements. In contrast, the Random Phase scheme, due to its lack of optimization for the IRS phase, offers limited performance improvement, further highlighting the importance of joint optimization.

[0054] Figure 7 This demonstrates the impact of the sensor node's minimum signal-to-noise ratio on the base station's transmit power. When the sensor node... When the power requirement of the Near-Optimal algorithm is increased from 4 dBm to 14 dBm, it only increases by 4 dB, while the Without IRS scheme increases by 6 dB. This indicates that IRS technology can effectively alleviate the high signal-to-noise ratio requirements of sensor nodes, thereby reducing the pressure on the main link power.

[0055] Figure 8 The number of IRS reflective elements is shown. N The relationship between base station transmit power and IRS. In all schemes with IRS, as the number of IRS reflective elements increases from 30 to 80, the base station transmit power decreases significantly, especially in the Near-Optimal scheme, where the base station transmit power drops from 14 dBm to 10 dBm. This indicates that more reflective elements can provide higher passive beamforming gain, allowing signal energy to be focused more efficiently on the target node, further reducing the base station's power requirements.

[0056] This invention proposes an IRS-assisted cellular communication framework integrating multiple AmBC-WBANs and investigates the joint optimization of active base station beamforming and passive IRS reflection coefficients, proposing an approximate optimal algorithm and a low-complexity implementation scheme. By decomposing the non-convex problem into alternating optimization subproblems and employing SDR and SCA techniques, the algorithm effectively minimizes the base station's transmission power while satisfying the QoS requirements of the central node and sensor nodes in multiple WBANs. Simulation results under different configurations confirm that the proposed method significantly reduces base station power consumption, and the low-complexity scheme achieves a good balance between performance and computational efficiency. This research provides valuable theoretical and algorithmic references for the design of IRS-enhanced co-existing radio systems. Future work will focus on robust optimization under imperfect CSI and the expansion of multi-IRS cooperative scenarios.

[0057] Example 2: An electronic device, comprising a memory and a processor; The memory is used to store computer programs; The processor is configured to implement the method described in Embodiment 1 when executing the computer program.

[0058] Example 3: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in Embodiment 1.

[0059] Example 4: A computer program product includes a computer program that, when executed by a processor, implements the method described in Example 1.

[0060] In the above embodiments, the reference to "this embodiment" in the specification indicates that a specific feature, structure, or characteristic described in connection with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple appearances of "this embodiment" do not necessarily refer to the same embodiment.

[0061] In the above embodiments, although the invention has been described in conjunction with specific embodiments thereof, many substitutions, modifications, and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory structures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed. The embodiments of the invention are intended to cover all such substitutions, modifications, and variations falling within the broad scope of the appended claims.

[0062] As will be understood by those skilled in the art, the computer-readable storage medium described in this embodiment allows for the implementation of all or part of the steps in the above method embodiments by computer program-related hardware. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0063] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver, and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication between them. The memory is used to store computer programs, the communication interface is used to perform communication, and the processor and the transceiver are used to run the computer programs, so that the electronic terminal performs the steps of the above method.

[0064] In this embodiment, the memory may include random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage device.

[0065] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0066] This invention can be used in a wide range of general-purpose or special-purpose computing system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.

[0067] This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0068] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An IRS-assisted AmBC wireless body area network resource optimization method, characterized in that: Includes the following steps: S1: Construct an IRS-enhanced downlink AmBC-WBAN communication system; S2: Under the constraint of ensuring the QoS of all nodes, minimize the transmission power of the BS and construct an optimization problem; S3: The original problem is decomposed into two sub-problems using an alternating iterative optimization algorithm: base station beamforming optimization and IRS reflection coefficient matrix optimization. For these two sub-problems, the positive semidefinite relaxation method (SDR) and the continuous approximation method (SCA) are used to solve them respectively. The solution is updated in an alternating manner until convergence.

2. The IRS-assisted AmBC wireless body area network resource optimization method according to claim 1, characterized in that: The IRS-enhanced downlink AmBC-WBAN communication system described in step S1 involves multiple AmBC-WBAN wireless body area networks based on environmental backscatter communication; the system includes a network equipped with M A base station BS with multiple antennas, which simultaneously serves as a time slot in the same time slot. K Provides service to 1,000 cellular users; each user is equipped with S Each internal or external sensor node forms a WBAN, with its handheld mobile terminal acting as the central node, while also directly receiving information from the BS, thus the system has a total of K A separate WBAN; The system adopts a symbiotic wireless communication architecture. The main signal transmitted by the base station has two functions: to facilitate the main communication link between the BS and the mobile terminal; and to achieve energy-efficient information transmission by backscattering through the AmBC sensor nodes in the WBAN. The system includes a component consisting of N An IRS consisting of programmable reflective units is used to enhance multiple communication links, including the link from the BS to the central node and the link from the sensor to the central node, all of which are optimized through intelligent beam control. Assumption and These represent the set of cellular users and the set of sensor nodes in each WBAN, respectively. The system operates in time-division multiple access mode, with each sensor node in a WBAN allocated a dedicated time slot, the duration of which is... Assuming all time slots have the same length, a normalized frame length is formed, i.e. The AmBC symbol period spans the main signal. L One symbol period, and the sensor nodes in each WBAN use on-off keyed modulation for backscatter communication; set up For the BS symbol period, The number of primary symbols spanned by one AmBC symbol; when using a normalized frame length and assuming each slot At that time, the normalized master symbol period must satisfy ; It is assumed that all Channel State Information (CSI) is fully known through channel estimation; in these assumptions, This indicates the channel from the BS to the IRS. Indicates the base station to the The channels of the central nodes of each WBAN, assuming within one time slot It is stable; assuming This represents the channel gain from the BS to all WBAN sensor nodes, where For base station to the The channel gain of each WBAN cellular user network sensor node, among which For base station to the The first WBAN cellular user network Channel gain of each sensing node; Indicates IRS to the k The channels of the central node of a WBAN cellular user network, assuming that within a time slot It is stable; assume This represents the channel gain from the IRS to the sensor nodes of all WBAN cellular user networks, where It is the IRS to the k The channel gain of the sensor nodes in a WBAN cellular user network, where For IRS to the k The first WBAN s Channel gain of each sensing node; Indicates the first s Complex gain of the AmBC reflection channel from each sensor node to its corresponding WBAN cellular user network center node; Assuming there is a line-of-sight (LoS) channel between the IRS and the BS, It is modeled as a Ricean fading channel, as shown below. （1） in, Rice factor, The line-of-sight portion of the channel. The channel matrix representing NLoS has elements that follow an independent and identically distributed complex Gaussian distribution with a mean of zero and a variance of 1; the remaining channels will experience independent Rayleigh fading. In the time slot s In this context, the main signal sent by the BS is represented as: （2） in, It is the base station in the time slot s China to the first k Beamforming vectors of WBANs Cellular user k The symbol, within each symbol time T, It is changing; in time slots Down, The changes in numerical values ​​do not affect the analysis process; In the time slot s In China, cellular users k The received signal is represented as: （3） in, It is the reflection coefficient of all sensor nodes. The elements follow independent and identically distributed additive white Gaussian noise with a mean of zero and a variance of . ; Indicates the first k The first WBAN s Information symbols of each sensor node; assuming and The probability of them occurring is equal. In the time slot s In this context, the reflection coefficient matrix of the IRS is expressed as: （4） in Indicates IRS reflective element n In the time slot s Phase shift in; the first k The signal-to-interference-plus-noise ratio (SINR) of the signal received by the central node of a WBAN is expressed by the following formula: （5） No. k The SINR of the signal received by the central node of each WBAN is further expressed by the following formula: （6） At the central node of each WBAN, continuous interference cancellation is used to remove components from the direct link and IRS link from the received composite signal, and then maximum ratio combining is used to reconstruct the remaining AmBC signal; k The first WBAN s The SINR of the signal decoded by each sensor node can be approximately expressed as: （7）。 3. The IRS-assisted AmBC wireless body area network resource optimization method according to claim 2, characterized in that: The optimization problem is expressed as: （8） Where C1 is the minimum QoS requirement for each sensor node across all WBANs, C1 represents the target SINR threshold; C2 represents the signal-to-noise ratio (SNR) of the central node in the WBAN cellular user network, which must exceed the target SNR. C3 specifies the feasible range of IRS reflection phase offset.

4. The IRS-assisted AmBC wireless body area network resource optimization method according to claim 3, characterized in that: In step S3, the base station beamforming optimization process includes: Treating the reflection coefficient matrix as constant, and optimizing only the beamforming vector scheme of the base station, the optimization problem becomes: （9） In iteration middle, By solving P1 and giving Optimization was performed using the semidefinite relaxation SDR method, which introduced auxiliary variables. , , , ,in, , ;when When the auxiliary variable is used, it is represented as: ;when When the auxiliary variable is used, it is represented as: ; auxiliary variables and Substituting (6) into the equation, we get: （10） Then formula (10) is transformed into: （11） Similarly, Substituting into (7), the constraint condition becomes: （12） After the above variable substitutions and equation transformations, (P2) is transformed into a convex problem (P2.1), which can be solved using the CVX toolbox: （13）。 5. The IRS-assisted AmBC wireless body area network resource optimization method according to claim 4, characterized in that: In step S3, the optimization process of the IRS reflection coefficient matrix includes: set up and ,definition and Equation (3) can be rewritten as: (14) in: ; In the In the next iteration, given , ,Will and Substituting into formula (7), we get: （15） in ; After the above transformation, constraint C1 in equation (8) is expressed as: （16） In equation (8), constraint C2 is expressed as: （17） Assumption The optimization problem can be further expressed as: （18） To obtain the rank-1 solution to problem P3, define Its equivalent rank-one constraint is expressed as: （19） An iterative approximation method based on the principal eigenvector is adopted, at the... In the next iteration, let For the previous solution The principal eigenvectors; the alternative problem is represented as: （20） By introducing auxiliary variables and the SCA method, the original problem P3 is transformed into a convex problem, which is solved using the standard convex optimization toolbox CVX. After decomposing the original problem into the two subproblems, these subproblems are solved iteratively until the result meets the convergence criterion. The obtained optimal value is considered as a lower bound, and feasible candidate solutions are extracted using eigenvalue decomposition (EVD). If the obtained solution does not satisfy the strict rank-one condition, Gaussian randomization is used to generate multiple randomized candidate solutions, and the one with the best performance is selected to ensure the feasibility and approximate optimality of the solution. Let Indicates the first The target value at the next iteration, i.e., the BS transmit power. When satisfied The algorithm terminates when the maximum number of iterations is reached.

6. The IRS-assisted AmBC wireless body area network resource optimization method according to claim 5, characterized in that: The update process of the IRS reflection coefficient is simplified through a low-complexity algorithm, including: The IRS is divided into the primary link IRS (P-IRS) and the AmBC link IRS (B-IRS); the P-IRS is used to improve the primary communication link, and the B-IRS is used to enhance the AmBC link. One reflective unit is assigned to B-IRS, and the rest... One reflective unit is assigned to the P-IRS, where Indicates the integer operation; Assumption For the base station to B-IRS link, For base station to P-IRS; For the link from B-IRS to the WBAN network central node, This refers to the link from P-IRS to the WBAN network central node. For B-IRS to the k The first WBAN cellular user network s Channels of each sensor node For P-IRS to the k The first WBAN cellular user network s Channels of each sensor node This represents the reflection coefficient matrix of the B-IRS. This represents the reflection coefficient matrix of the P-IRS. Indicates time slot s The reflection coefficient vector at the B-IRS in the middle, Indicates time slot s The reflection coefficient vector at the P-IRS; To optimize the IRS reflection coefficient matrix, equation (6) is equivalently restated as: （21） in, ;when and hour The situation is consistent, (21) with To analyze and obtain P-IRS ; Equation (7) is equivalently restated as: （22） in, Extracting from the solution of P2 using eigenvalue decomposition , All signal components in the molecule are coherently combined to make maximize; Configured to maximize the corresponding equation by the following formula The received signal strength is ; Then put and Constructed into : （23） in, .

7. An electronic device, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor is configured to, when executing the computer program, implement the IRS-assisted AmBC wireless body area network resource optimization method as described in any one of claims 1-6.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, implements the IRS-assisted AmBC wireless body area network resource optimization method as described in any one of claims 1-6.

9. A computer program product, characterized in that: Includes a computer program that, when executed by a processor, implements the IRS-assisted AmBC wireless body area network resource optimization method as described in any one of claims 1-6.

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